Forecasting of commercial sales with large scale Gaussian Processes
This work provides a decision-making tool for management in the fast-moving consumer goods industry, but it is incremental as it reviews and evaluates existing approaches rather than introducing new methods.
The paper tackles the problem of forecasting commercial sales in the fast-moving consumer goods industry using Gaussian Processes, addressing challenges like large data size and high dimensionality, and demonstrates the value of these models as a decision-making tool for management.
This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.